This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All of the images used for the research are uploaded to the Github repository. Received: DecemAccepted: SeptemPublished: October 11, 2018Ĭopyright: © 2018 Iqbal et al. Queen Mary University of London, UNITED KINGDOM
LOCATION TRACKING WITH RADBEACON BLUETOOTH
(2018) Accurate real time localization tracking in a clinical environment using Bluetooth Low Energy and deep learning. Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety.Ĭitation: Iqbal Z, Luo D, Henry P, Kazemifar S, Rozario T, Yan Y, et al. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 95%). By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%.
The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation. This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace.
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